In [1]}, a recurrent neural network - the controller - is trained with reinforcement learning where the outputs of this RNN determine the architecture of another neural network - the child network - that is used for prediction. The child network is configured by the controller and it is trained using a dataset. The obtained performance is used as reward for training the controller. This approach is originally developed for supervised learning, although deriving optimal architecture of a neural network can be helpful for other domains.
